class_docstr: Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
  Args:
      is_dlib(bool, optional): If true, dlib is used for detecting faces.
      prefix(str, optional): If run at another folder, the absolute path is needed to load the data.
class_docstr_tok: ["Joint","3D","Face","Reconstruction","and","Dense","Alignment","with","Position","Map","Regression","Network","Args",":","is_dlib","(","bool",",","optional",")",":","If","true",",","dlib","is","used","for","detecting","faces",".","prefix","(","str",",","optional",")",":","If","run","at","another","folder",",","the","absolute","path","is","needed","to","load","the","data","."]
docstr: The core of out method: regress the position map of a given image.
  Args:
      image: (256,256,3) array. value range: 0~1
  Returns:
      pos: the 3D position map. (256, 256, 3) array.
docstr_tok: ["The","core","of","out","method",":","regress","the","position","map","of","a","given","image",".","Args",":","image",":","(","256,256,3",")","array",".","value","range",":","0~1","Returns",":","pos",":","the","3D","position","map",".","(","256",",","256",",","3",")","array","."]
code: def net_forward(self, image):
      """The core of out method: regress the position map of a given image.
      Args:
          image: (256,256,3) array. value range: 0~1
      Returns:
          pos: the 3D position map. (256, 256, 3) array.
      """
      return self.pos_predictor.predict(image)
  
file_path: PaddleHub/modules/image/image_processing/prnet/api.py
imports: import os
  from time import time
  import numpy
  import paddle
  from skimage.io import imread
  from skimage.io import imsave
  from skimage.transform import estimate_transform
  from skimage.transform import warp
  from .predictor import PosPrediction
label_desc: Use image node_id 4 for calling the PRN obj's underlying member method code with example usage: obj.net_forward(image) and returns: self